Instructions to use phucd/vilt-gqa-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phucd/vilt-gqa-ft with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="phucd/vilt-gqa-ft")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("phucd/vilt-gqa-ft") model = AutoModelForVisualQuestionAnswering.from_pretrained("phucd/vilt-gqa-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from phucd/vilt-gqa-ft: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/phucd/vilt-gqa-ft/resolve/main/training_args.bin
- Command line
-
hf download hf://phucd/vilt-gqa-ft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/phucd/vilt-gqa-ft/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- c994ea5bfdb0c101fad405723f099c0cff86117c6388709414d4d7e4b7540292
- Size of remote file:
- 5.3 kB
- SHA256:
- d0af64cea51a8a1f962973bec26f5bef9a3ccb49eeafebc2fa0fe683e6f9a6ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.